Long-Short Term Memory Networks and Synthetic Data for Heavy Vehicle Rollover Prevention

Heavy vehicle rollover plays a pivotal role in road safety scenarios. Numerous researchers addressed the topic, with particular focus on drivers related injuries. Considering the same and other connected implications, the necessity for techniques able to estimate and predict overturning eventualities appears evident. Different methodologies were explored, with notable achievements obtained by neural network-based algorithms. At the same time, their heavy requirements in terms of data needs to be addressed to allow practical applications in terms of time and costs. Consequently, exploring the interaction between simulation and experimental data becomes extremely important, motivating the methodology proposed by this paper. In details, an heavy vehicle model was designed in IPG Carmaker, while experimental data on its physical alter ego were acquired. This led to the generation of a synthetic dataset and the collection of an empirical one. Both were used to define a Long Short-Term Memory architecture, with a dual purpose. First, as typical rollover indicator, estimate the vehicle roll angle. Second, compare the performance of the neural networks, aiming to obtain at least the same order of magnitude in terms of RMSE, MSE and MAE. The goal was to demonstrate that synthetic data can not only be used in combination with real data, but also as substitutes able to address time and cost constraints inevitably linked to the latter, allowing more efficient experiments for overtipping prevention.